Overview

Dataset statistics

Number of variables22
Number of observations744
Missing cells6576
Missing cells (%)40.2%
Duplicate rows5
Duplicate rows (%)0.7%
Total size in memory133.7 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported1
Categorical5
Numeric14

Alerts

Division has constant value ""Constant
Qualifier has constant value ""Constant
L1 Benchmark (s) has constant value ""Constant
Dataset has 5 (0.7%) duplicate rowsDuplicates
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 2 other fieldsHigh correlation
Chad1000x (s) is highly overall correlated with Filthy 50 (s) and 3 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Grace (s)High correlation
Filthy 50 (s) is highly overall correlated with Chad1000x (s)High correlation
Fran (s) is highly overall correlated with Chad1000x (s) and 2 other fieldsHigh correlation
Games_Level is highly overall correlated with RegionHigh correlation
Grace (s) is highly overall correlated with Clean and Jerk (lbs) and 2 other fieldsHigh correlation
Helen (s) is highly overall correlated with Fran (s) and 1 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Fran (s)High correlation
Region is highly overall correlated with Games_LevelHigh correlation
Run 5k (s) is highly overall correlated with Chad1000x (s) and 1 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Sprint 400m (s) is highly overall correlated with Chad1000x (s) and 2 other fieldsHigh correlation
Affiliate has 61 (8.2%) missing valuesMissing
Country has 744 (100.0%) missing valuesMissing
Back Squat (lbs) has 58 (7.8%) missing valuesMissing
Clean and Jerk (lbs) has 59 (7.9%) missing valuesMissing
Deadlift (lbs) has 63 (8.5%) missing valuesMissing
Snatch (lbs) has 65 (8.7%) missing valuesMissing
Fight Gone Bad has 586 (78.8%) missing valuesMissing
Max Pull-ups has 477 (64.1%) missing valuesMissing
Chad1000x (s) has 735 (98.8%) missing valuesMissing
L1 Benchmark (s) has 743 (99.9%) missing valuesMissing
Filthy 50 (s) has 642 (86.3%) missing valuesMissing
Fran (s) has 330 (44.4%) missing valuesMissing
Grace (s) has 363 (48.8%) missing valuesMissing
Helen (s) has 534 (71.8%) missing valuesMissing
Run 5k (s) has 511 (68.7%) missing valuesMissing
Sprint 400m (s) has 605 (81.3%) missing valuesMissing
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-17 20:26:28.195295
Analysis finished2024-02-17 20:26:49.184233
Duration20.99 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct736
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:49.330803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length14.037634
Min length8

Characters and Unicode

Total characters10444
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique728 ?
Unique (%)97.8%

Sample

1st rowMichelle Merand
2nd rowAngelique Connoway
3rd rowLani Venter
4th rowDina Swift
5th rowLeilani Tison
ValueCountFrequency (%)
jessica 13
 
0.9%
sarah 11
 
0.7%
rachel 10
 
0.7%
lauren 10
 
0.7%
stephanie 9
 
0.6%
megan 8
 
0.5%
emma 8
 
0.5%
amanda 8
 
0.5%
taylor 8
 
0.5%
kathryn 7
 
0.5%
Other values (1117) 1420
93.9%
2024-02-17T15:26:49.702265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1175
 
11.3%
e 1040
 
10.0%
768
 
7.4%
n 744
 
7.1%
i 735
 
7.0%
r 666
 
6.4%
l 660
 
6.3%
o 475
 
4.5%
s 383
 
3.7%
t 356
 
3.4%
Other values (54) 3442
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8097
77.5%
Uppercase Letter 1556
 
14.9%
Space Separator 768
 
7.4%
Dash Punctuation 18
 
0.2%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1175
14.5%
e 1040
12.8%
n 744
9.2%
i 735
9.1%
r 666
8.2%
l 660
8.2%
o 475
 
5.9%
s 383
 
4.7%
t 356
 
4.4%
h 273
 
3.4%
Other values (25) 1590
19.6%
Uppercase Letter
ValueCountFrequency (%)
M 169
 
10.9%
S 150
 
9.6%
A 127
 
8.2%
C 120
 
7.7%
K 103
 
6.6%
B 103
 
6.6%
J 94
 
6.0%
L 88
 
5.7%
H 85
 
5.5%
D 66
 
4.2%
Other values (16) 451
29.0%
Space Separator
ValueCountFrequency (%)
768
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
' 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9653
92.4%
Common 791
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1175
 
12.2%
e 1040
 
10.8%
n 744
 
7.7%
i 735
 
7.6%
r 666
 
6.9%
l 660
 
6.8%
o 475
 
4.9%
s 383
 
4.0%
t 356
 
3.7%
h 273
 
2.8%
Other values (51) 3146
32.6%
Common
ValueCountFrequency (%)
768
97.1%
- 18
 
2.3%
' 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10428
99.8%
None 16
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1175
 
11.3%
e 1040
 
10.0%
768
 
7.4%
n 744
 
7.1%
i 735
 
7.0%
r 666
 
6.4%
l 660
 
6.3%
o 475
 
4.6%
s 383
 
3.7%
t 356
 
3.4%
Other values (45) 3426
32.9%
None
ValueCountFrequency (%)
é 5
31.2%
ö 3
18.8%
á 2
 
12.5%
ñ 1
 
6.2%
ä 1
 
6.2%
č 1
 
6.2%
ř 1
 
6.2%
ë 1
 
6.2%
ø 1
 
6.2%

Affiliate
Text

MISSING 

Distinct607
Distinct (%)88.9%
Missing61
Missing (%)8.2%
Memory size11.6 KiB
2024-02-17T15:26:49.972796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length17.181552
Min length11

Characters and Unicode

Total characters11735
Distinct characters70
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique543 ?
Unique (%)79.5%

Sample

1st rowCrossFit FBDV
2nd rowCrossFit FBDV
3rd rowCrossFit 10 Star
4th row6th Element CrossFit
5th rowCrossFit U1
ValueCountFrequency (%)
crossfit 683
42.2%
city 13
 
0.8%
the 10
 
0.6%
park 9
 
0.6%
iron 6
 
0.4%
north 5
 
0.3%
la 5
 
0.3%
east 5
 
0.3%
koda 5
 
0.3%
valley 5
 
0.3%
Other values (714) 871
53.9%
2024-02-17T15:26:50.350945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 1569
13.4%
o 1040
 
8.9%
r 1018
 
8.7%
i 1004
 
8.6%
t 979
 
8.3%
934
 
8.0%
C 778
 
6.6%
F 757
 
6.5%
e 504
 
4.3%
a 435
 
3.7%
Other values (60) 2717
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8198
69.9%
Uppercase Letter 2434
 
20.7%
Space Separator 934
 
8.0%
Decimal Number 149
 
1.3%
Other Punctuation 12
 
0.1%
Dash Punctuation 7
 
0.1%
Format 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1569
19.1%
o 1040
12.7%
r 1018
12.4%
i 1004
12.2%
t 979
11.9%
e 504
 
6.1%
a 435
 
5.3%
n 320
 
3.9%
l 268
 
3.3%
u 130
 
1.6%
Other values (19) 931
11.4%
Uppercase Letter
ValueCountFrequency (%)
C 778
32.0%
F 757
31.1%
S 99
 
4.1%
B 71
 
2.9%
T 70
 
2.9%
M 65
 
2.7%
P 59
 
2.4%
L 49
 
2.0%
R 49
 
2.0%
H 43
 
1.8%
Other values (16) 394
16.2%
Decimal Number
ValueCountFrequency (%)
1 36
24.2%
0 25
16.8%
4 15
10.1%
2 14
 
9.4%
9 14
 
9.4%
3 13
 
8.7%
8 10
 
6.7%
6 8
 
5.4%
7 7
 
4.7%
5 7
 
4.7%
Other Punctuation
ValueCountFrequency (%)
' 6
50.0%
. 6
50.0%
Space Separator
ValueCountFrequency (%)
934
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Format
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10632
90.6%
Common 1103
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1569
14.8%
o 1040
9.8%
r 1018
9.6%
i 1004
9.4%
t 979
9.2%
C 778
 
7.3%
F 757
 
7.1%
e 504
 
4.7%
a 435
 
4.1%
n 320
 
3.0%
Other values (45) 2228
21.0%
Common
ValueCountFrequency (%)
934
84.7%
1 36
 
3.3%
0 25
 
2.3%
4 15
 
1.4%
2 14
 
1.3%
9 14
 
1.3%
3 13
 
1.2%
8 10
 
0.9%
6 8
 
0.7%
7 7
 
0.6%
Other values (5) 27
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11728
99.9%
None 6
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1569
13.4%
o 1040
 
8.9%
r 1018
 
8.7%
i 1004
 
8.6%
t 979
 
8.3%
934
 
8.0%
C 778
 
6.6%
F 757
 
6.5%
e 504
 
4.3%
a 435
 
3.7%
Other values (56) 2710
23.1%
None
ValueCountFrequency (%)
ä 4
66.7%
ö 1
 
16.7%
é 1
 
16.7%
Punctuation
ValueCountFrequency (%)
1
100.0%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing744
Missing (%)100.0%
Memory size11.6 KiB

Region
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
North America East
304 
North America West
203 
Europe
124 
Oceania
60 
South America
 
25
Other values (2)
 
28

Length

Max length18
Median length18
Mean length14.447581
Min length4

Characters and Unicode

Total characters10749
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica

Common Values

ValueCountFrequency (%)
North America East 304
40.9%
North America West 203
27.3%
Europe 124
16.7%
Oceania 60
 
8.1%
South America 25
 
3.4%
Asia 17
 
2.3%
Africa 11
 
1.5%

Length

2024-02-17T15:26:50.516105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:26:50.630495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
america 532
29.8%
north 507
28.4%
east 304
17.0%
west 203
 
11.4%
europe 124
 
7.0%
oceania 60
 
3.4%
south 25
 
1.4%
asia 17
 
1.0%
africa 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 1174
10.9%
t 1039
 
9.7%
1039
 
9.7%
a 984
 
9.2%
e 919
 
8.5%
o 656
 
6.1%
i 620
 
5.8%
c 603
 
5.6%
A 560
 
5.2%
h 532
 
4.9%
Other values (11) 2623
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7927
73.7%
Uppercase Letter 1783
 
16.6%
Space Separator 1039
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1174
14.8%
t 1039
13.1%
a 984
12.4%
e 919
11.6%
o 656
8.3%
i 620
7.8%
c 603
7.6%
h 532
6.7%
m 532
6.7%
s 524
6.6%
Other values (4) 344
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 560
31.4%
N 507
28.4%
E 428
24.0%
W 203
 
11.4%
O 60
 
3.4%
S 25
 
1.4%
Space Separator
ValueCountFrequency (%)
1039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9710
90.3%
Common 1039
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1174
12.1%
t 1039
10.7%
a 984
10.1%
e 919
9.5%
o 656
 
6.8%
i 620
 
6.4%
c 603
 
6.2%
A 560
 
5.8%
h 532
 
5.5%
m 532
 
5.5%
Other values (10) 2091
21.5%
Common
ValueCountFrequency (%)
1039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1174
10.9%
t 1039
 
9.7%
1039
 
9.7%
a 984
 
9.2%
e 919
 
8.5%
o 656
 
6.1%
i 620
 
5.8%
c 603
 
5.6%
A 560
 
5.2%
h 532
 
4.9%
Other values (11) 2623
24.4%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Women
744 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3720
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWomen
2nd rowWomen
3rd rowWomen
4th rowWomen
5th rowWomen

Common Values

ValueCountFrequency (%)
Women 744
100.0%

Length

2024-02-17T15:26:50.756382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:26:50.844781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
women 744
100.0%

Most occurring characters

ValueCountFrequency (%)
W 744
20.0%
o 744
20.0%
m 744
20.0%
e 744
20.0%
n 744
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2976
80.0%
Uppercase Letter 744
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 744
25.0%
m 744
25.0%
e 744
25.0%
n 744
25.0%
Uppercase Letter
ValueCountFrequency (%)
W 744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 744
20.0%
o 744
20.0%
m 744
20.0%
e 744
20.0%
n 744
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 744
20.0%
o 744
20.0%
m 744
20.0%
e 744
20.0%
n 744
20.0%

Rank
Real number (ℝ)

Distinct573
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean649.4879
Minimum6
Maximum1813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:50.955857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile73
Q1226.75
median523.5
Q31017
95-th percentile1602
Maximum1813
Range1807
Interquartile range (IQR)790.25

Descriptive statistics

Standard deviation487.05838
Coefficient of variation (CV)0.74991139
Kurtosis-0.6191477
Mean649.4879
Median Absolute Deviation (MAD)360
Skewness0.65576273
Sum483219
Variance237225.86
MonotonicityNot monotonic
2024-02-17T15:26:51.090239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1813 8
 
1.1%
1430 7
 
0.9%
1137 7
 
0.9%
143 4
 
0.5%
736 3
 
0.4%
1062 3
 
0.4%
232 3
 
0.4%
971 3
 
0.4%
386 3
 
0.4%
144 3
 
0.4%
Other values (563) 700
94.1%
ValueCountFrequency (%)
6 1
0.1%
9 1
0.1%
10 1
0.1%
11 2
0.3%
12 1
0.1%
17 1
0.1%
23 1
0.1%
25 1
0.1%
26 2
0.3%
27 2
0.3%
ValueCountFrequency (%)
1813 8
1.1%
1806 1
 
0.1%
1805 1
 
0.1%
1804 1
 
0.1%
1801 1
 
0.1%
1782 1
 
0.1%
1780 1
 
0.1%
1771 1
 
0.1%
1770 1
 
0.1%
1764 1
 
0.1%

Games_Level
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
North America East
304 
North America West
203 
Europe
124 
Oceania
60 
South America
 
25
Other values (2)
 
28

Length

Max length18
Median length18
Mean length14.447581
Min length4

Characters and Unicode

Total characters10749
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica

Common Values

ValueCountFrequency (%)
North America East 304
40.9%
North America West 203
27.3%
Europe 124
16.7%
Oceania 60
 
8.1%
South America 25
 
3.4%
Asia 17
 
2.3%
Africa 11
 
1.5%

Length

2024-02-17T15:26:51.221143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:26:51.335144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
america 532
29.8%
north 507
28.4%
east 304
17.0%
west 203
 
11.4%
europe 124
 
7.0%
oceania 60
 
3.4%
south 25
 
1.4%
asia 17
 
1.0%
africa 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 1174
10.9%
t 1039
 
9.7%
1039
 
9.7%
a 984
 
9.2%
e 919
 
8.5%
o 656
 
6.1%
i 620
 
5.8%
c 603
 
5.6%
A 560
 
5.2%
h 532
 
4.9%
Other values (11) 2623
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7927
73.7%
Uppercase Letter 1783
 
16.6%
Space Separator 1039
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1174
14.8%
t 1039
13.1%
a 984
12.4%
e 919
11.6%
o 656
8.3%
i 620
7.8%
c 603
7.6%
h 532
6.7%
m 532
6.7%
s 524
6.6%
Other values (4) 344
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 560
31.4%
N 507
28.4%
E 428
24.0%
W 203
 
11.4%
O 60
 
3.4%
S 25
 
1.4%
Space Separator
ValueCountFrequency (%)
1039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9710
90.3%
Common 1039
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1174
12.1%
t 1039
10.7%
a 984
10.1%
e 919
9.5%
o 656
 
6.8%
i 620
 
6.4%
c 603
 
6.2%
A 560
 
5.8%
h 532
 
5.5%
m 532
 
5.5%
Other values (10) 2091
21.5%
Common
ValueCountFrequency (%)
1039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1174
10.9%
t 1039
 
9.7%
1039
 
9.7%
a 984
 
9.2%
e 919
 
8.5%
o 656
 
6.1%
i 620
 
5.8%
c 603
 
5.6%
A 560
 
5.2%
h 532
 
4.9%
Other values (11) 2623
24.4%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
quarterfinals
744 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9672
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquarterfinals
2nd rowquarterfinals
3rd rowquarterfinals
4th rowquarterfinals
5th rowquarterfinals

Common Values

ValueCountFrequency (%)
quarterfinals 744
100.0%

Length

2024-02-17T15:26:51.463218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:26:51.553434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
quarterfinals 744
100.0%

Most occurring characters

ValueCountFrequency (%)
a 1488
15.4%
r 1488
15.4%
q 744
7.7%
u 744
7.7%
t 744
7.7%
e 744
7.7%
f 744
7.7%
i 744
7.7%
n 744
7.7%
l 744
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9672
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1488
15.4%
r 1488
15.4%
q 744
7.7%
u 744
7.7%
t 744
7.7%
e 744
7.7%
f 744
7.7%
i 744
7.7%
n 744
7.7%
l 744
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 9672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1488
15.4%
r 1488
15.4%
q 744
7.7%
u 744
7.7%
t 744
7.7%
e 744
7.7%
f 744
7.7%
i 744
7.7%
n 744
7.7%
l 744
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1488
15.4%
r 1488
15.4%
q 744
7.7%
u 744
7.7%
t 744
7.7%
e 744
7.7%
f 744
7.7%
i 744
7.7%
n 744
7.7%
l 744
7.7%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct109
Distinct (%)15.9%
Missing58
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean259.28782
Minimum90
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:51.653730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile205
Q1235
median260
Q3285
95-th percentile320
Maximum365
Range275
Interquartile range (IQR)50

Descriptive statistics

Standard deviation36.938617
Coefficient of variation (CV)0.14246183
Kurtosis0.87306881
Mean259.28782
Median Absolute Deviation (MAD)25
Skewness-0.1566832
Sum177871.45
Variance1364.4614
MonotonicityNot monotonic
2024-02-17T15:26:51.781168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265 46
 
6.2%
255 34
 
4.6%
235 30
 
4.0%
250 25
 
3.4%
230 24
 
3.2%
300 21
 
2.8%
285 21
 
2.8%
260 21
 
2.8%
270 20
 
2.7%
245 19
 
2.6%
Other values (99) 425
57.1%
(Missing) 58
 
7.8%
ValueCountFrequency (%)
90 1
 
0.1%
110 1
 
0.1%
115 1
 
0.1%
132.2772 1
 
0.1%
160 1
 
0.1%
165.3465 1
 
0.1%
175 1
 
0.1%
176.3696 1
 
0.1%
185 4
0.5%
187.3927 2
0.3%
ValueCountFrequency (%)
365 2
0.3%
360 1
 
0.1%
352.7392 1
 
0.1%
345 2
0.3%
341.7161 1
 
0.1%
340 3
0.4%
337.30686 1
 
0.1%
335 4
0.5%
330.693 1
 
0.1%
330 1
 
0.1%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)15.6%
Missing59
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean195.87015
Minimum75
Maximum509.26722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:51.911157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile155
Q1180
median195
Q3215
95-th percentile235.71547
Maximum509.26722
Range434.26722
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.416826
Coefficient of variation (CV)0.14507992
Kurtosis21.69812
Mean195.87015
Median Absolute Deviation (MAD)15
Skewness1.687088
Sum134171.05
Variance807.516
MonotonicityNot monotonic
2024-02-17T15:26:53.374982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 51
 
6.9%
205 41
 
5.5%
215 39
 
5.2%
200 35
 
4.7%
190 32
 
4.3%
195 31
 
4.2%
180 29
 
3.9%
175 27
 
3.6%
165 22
 
3.0%
210 21
 
2.8%
Other values (97) 357
48.0%
(Missing) 59
 
7.9%
ValueCountFrequency (%)
75 2
0.3%
92 1
 
0.1%
120 1
 
0.1%
121.2541 1
 
0.1%
132.2772 1
 
0.1%
135 3
0.4%
138.89106 2
0.3%
140 1
 
0.1%
143.3003 2
0.3%
145 4
0.5%
ValueCountFrequency (%)
509.26722 1
 
0.1%
264.5544 1
 
0.1%
260 3
0.4%
258 1
 
0.1%
255.73592 1
 
0.1%
255 3
0.4%
253.5313 1
 
0.1%
253 1
 
0.1%
251.32668 1
 
0.1%
250 2
0.3%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)16.2%
Missing63
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean309.61251
Minimum198.4158
Maximum495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:53.500236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum198.4158
5-th percentile245
Q1285
median306.44218
Q3332.89762
95-th percentile375
Maximum495
Range296.5842
Interquartile range (IQR)47.89762

Descriptive statistics

Standard deviation39.214127
Coefficient of variation (CV)0.1266555
Kurtosis1.1492701
Mean309.61251
Median Absolute Deviation (MAD)24.25082
Skewness0.3566545
Sum210846.12
Variance1537.7478
MonotonicityNot monotonic
2024-02-17T15:26:53.630918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 51
 
6.9%
315 43
 
5.8%
305 35
 
4.7%
325 32
 
4.3%
275 26
 
3.5%
286.6006 20
 
2.7%
335 20
 
2.7%
295 20
 
2.7%
285 17
 
2.3%
265 17
 
2.3%
Other values (100) 400
53.8%
(Missing) 63
 
8.5%
ValueCountFrequency (%)
198.4158 1
 
0.1%
200 1
 
0.1%
209.4389 1
 
0.1%
210 1
 
0.1%
215 2
 
0.3%
220.462 3
0.4%
225 3
0.4%
230 1
 
0.1%
231.4851 2
 
0.3%
235 7
0.9%
ValueCountFrequency (%)
495 1
 
0.1%
456.35634 1
 
0.1%
440.924 3
0.4%
410 1
 
0.1%
405 6
0.8%
400 3
0.4%
396.8316 3
0.4%
395 1
 
0.1%
390 3
0.4%
385.8085 2
 
0.3%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)14.6%
Missing65
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean151.53196
Minimum2.20462
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:53.763670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.20462
5-th percentile115
Q1135.84322
median150
Q3165.3465
95-th percentile190
Maximum215
Range212.79538
Interquartile range (IQR)29.50328

Descriptive statistics

Standard deviation23.559229
Coefficient of variation (CV)0.15547367
Kurtosis2.4874723
Mean151.53196
Median Absolute Deviation (MAD)15
Skewness-0.43385348
Sum102890.2
Variance555.03729
MonotonicityNot monotonic
2024-02-17T15:26:53.897824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 63
 
8.5%
155 45
 
6.0%
160 37
 
5.0%
165 33
 
4.4%
135 32
 
4.3%
175 31
 
4.2%
140 30
 
4.0%
125 25
 
3.4%
150 21
 
2.8%
143.3003 21
 
2.8%
Other values (89) 341
45.8%
(Missing) 65
 
8.7%
ValueCountFrequency (%)
2.20462 1
 
0.1%
55 1
 
0.1%
58 1
 
0.1%
85 1
 
0.1%
88.1848 1
 
0.1%
95 1
 
0.1%
99.2079 1
 
0.1%
100 2
0.3%
105 4
0.5%
105.82176 1
 
0.1%
ValueCountFrequency (%)
215 1
 
0.1%
210 1
 
0.1%
209.4389 1
 
0.1%
207 1
 
0.1%
205 9
1.2%
200 5
0.7%
198.4158 2
 
0.3%
196.21118 1
 
0.1%
195 3
 
0.4%
194.00656 1
 
0.1%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct95
Distinct (%)60.1%
Missing586
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean328.25949
Minimum185
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:54.026077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum185
5-th percentile237.7
Q1305.25
median327.5
Q3352
95-th percentile411.15
Maximum440
Range255
Interquartile range (IQR)46.75

Descriptive statistics

Standard deviation46.075703
Coefficient of variation (CV)0.14036366
Kurtosis0.70679769
Mean328.25949
Median Absolute Deviation (MAD)24
Skewness-0.015648457
Sum51865
Variance2122.9705
MonotonicityNot monotonic
2024-02-17T15:26:54.174137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
328 5
 
0.7%
331 4
 
0.5%
317 4
 
0.5%
324 4
 
0.5%
330 4
 
0.5%
400 3
 
0.4%
293 3
 
0.4%
329 3
 
0.4%
309 3
 
0.4%
320 3
 
0.4%
Other values (85) 122
 
16.4%
(Missing) 586
78.8%
ValueCountFrequency (%)
185 1
0.1%
219 1
0.1%
221 1
0.1%
227 1
0.1%
228 1
0.1%
233 1
0.1%
235 1
0.1%
236 1
0.1%
238 1
0.1%
246 1
0.1%
ValueCountFrequency (%)
440 2
0.3%
436 1
 
0.1%
434 1
 
0.1%
429 1
 
0.1%
426 1
 
0.1%
412 2
0.3%
411 1
 
0.1%
408 2
0.3%
401 1
 
0.1%
400 3
0.4%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)21.0%
Missing477
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean31.895131
Minimum6
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:54.306628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q122
median30
Q340
95-th percentile55
Maximum85
Range79
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.704873
Coefficient of variation (CV)0.42968543
Kurtosis0.55330007
Mean31.895131
Median Absolute Deviation (MAD)10
Skewness0.618717
Sum8516
Variance187.82355
MonotonicityNot monotonic
2024-02-17T15:26:54.447047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 30
 
4.0%
40 20
 
2.7%
25 18
 
2.4%
50 17
 
2.3%
20 16
 
2.2%
35 12
 
1.6%
24 7
 
0.9%
32 7
 
0.9%
38 6
 
0.8%
21 6
 
0.8%
Other values (46) 128
 
17.2%
(Missing) 477
64.1%
ValueCountFrequency (%)
6 1
 
0.1%
7 1
 
0.1%
8 2
 
0.3%
9 1
 
0.1%
10 4
0.5%
11 4
0.5%
12 5
0.7%
13 5
0.7%
14 3
0.4%
15 6
0.8%
ValueCountFrequency (%)
85 1
 
0.1%
80 1
 
0.1%
69 1
 
0.1%
65 2
0.3%
63 1
 
0.1%
62 1
 
0.1%
60 2
0.3%
58 1
 
0.1%
56 1
 
0.1%
55 4
0.5%

Chad1000x (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing735
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean4057
Minimum3255
Maximum5206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:54.567598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3255
5-th percentile3392.2
Q13730
median4030
Q34094
95-th percentile4943.2
Maximum5206
Range1951
Interquartile range (IQR)364

Descriptive statistics

Standard deviation562.05227
Coefficient of variation (CV)0.13853889
Kurtosis1.5242972
Mean4057
Median Absolute Deviation (MAD)300
Skewness0.89470447
Sum36513
Variance315902.75
MonotonicityNot monotonic
2024-02-17T15:26:54.668978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3255 1
 
0.1%
4020 1
 
0.1%
5206 1
 
0.1%
3598 1
 
0.1%
4031 1
 
0.1%
4030 1
 
0.1%
3730 1
 
0.1%
4549 1
 
0.1%
4094 1
 
0.1%
(Missing) 735
98.8%
ValueCountFrequency (%)
3255 1
0.1%
3598 1
0.1%
3730 1
0.1%
4020 1
0.1%
4030 1
0.1%
4031 1
0.1%
4094 1
0.1%
4549 1
0.1%
5206 1
0.1%
ValueCountFrequency (%)
5206 1
0.1%
4549 1
0.1%
4094 1
0.1%
4031 1
0.1%
4030 1
0.1%
4020 1
0.1%
3730 1
0.1%
3598 1
0.1%
3255 1
0.1%

L1 Benchmark (s)
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing743
Missing (%)99.9%
Memory size11.6 KiB
288.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row288.0

Common Values

ValueCountFrequency (%)
288.0 1
 
0.1%
(Missing) 743
99.9%

Length

2024-02-17T15:26:54.786348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:26:54.869701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
288.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
8 2
40.0%
2 1
20.0%
. 1
20.0%
0 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
80.0%
Other Punctuation 1
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 2
50.0%
2 1
25.0%
0 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 2
40.0%
2 1
20.0%
. 1
20.0%
0 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 2
40.0%
2 1
20.0%
. 1
20.0%
0 1
20.0%

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92
Distinct (%)90.2%
Missing642
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean1467.6471
Minimum935
Maximum2327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:54.975372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum935
5-th percentile1088.55
Q11288.5
median1433
Q31640.75
95-th percentile1846.35
Maximum2327
Range1392
Interquartile range (IQR)352.25

Descriptive statistics

Standard deviation265.02093
Coefficient of variation (CV)0.18057538
Kurtosis0.89795433
Mean1467.6471
Median Absolute Deviation (MAD)170.5
Skewness0.66823215
Sum149700
Variance70236.092
MonotonicityNot monotonic
2024-02-17T15:26:55.119539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1272 3
 
0.4%
1630 2
 
0.3%
1916 2
 
0.3%
1371 2
 
0.3%
1434 2
 
0.3%
1454 2
 
0.3%
1785 2
 
0.3%
2327 2
 
0.3%
1066 2
 
0.3%
1152 1
 
0.1%
Other values (82) 82
 
11.0%
(Missing) 642
86.3%
ValueCountFrequency (%)
935 1
0.1%
960 1
0.1%
1016 1
0.1%
1066 2
0.3%
1088 1
0.1%
1099 1
0.1%
1110 1
0.1%
1121 1
0.1%
1144 1
0.1%
1145 1
0.1%
ValueCountFrequency (%)
2327 2
0.3%
2098 1
0.1%
1916 2
0.3%
1847 1
0.1%
1834 1
0.1%
1819 1
0.1%
1816 1
0.1%
1800 1
0.1%
1785 2
0.3%
1771 1
0.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)46.1%
Missing330
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean224.72464
Minimum113
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:55.265801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile133.65
Q1170
median209
Q3259.25
95-th percentile359
Maximum846
Range733
Interquartile range (IQR)89.25

Descriptive statistics

Standard deviation84.136299
Coefficient of variation (CV)0.3743973
Kurtosis14.537699
Mean224.72464
Median Absolute Deviation (MAD)42
Skewness2.6975443
Sum93036
Variance7078.9167
MonotonicityNot monotonic
2024-02-17T15:26:55.405968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 10
 
1.3%
240 7
 
0.9%
157 6
 
0.8%
171 6
 
0.8%
180 6
 
0.8%
179 6
 
0.8%
176 6
 
0.8%
168 6
 
0.8%
170 5
 
0.7%
252 5
 
0.7%
Other values (181) 351
47.2%
(Missing) 330
44.4%
ValueCountFrequency (%)
113 1
 
0.1%
118 2
0.3%
119 2
0.3%
122 2
0.3%
124 2
0.3%
125 1
 
0.1%
127 1
 
0.1%
128 3
0.4%
129 2
0.3%
131 1
 
0.1%
ValueCountFrequency (%)
846 2
0.3%
587 1
0.1%
557 1
0.1%
490 1
0.1%
455 1
0.1%
430 1
0.1%
425 1
0.1%
415 1
0.1%
413 1
0.1%
405 1
0.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct148
Distinct (%)38.8%
Missing363
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean147.43832
Minimum62
Maximum444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:55.545582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile88
Q1114
median138
Q3167
95-th percentile232
Maximum444
Range382
Interquartile range (IQR)53

Descriptive statistics

Standard deviation50.176525
Coefficient of variation (CV)0.34032214
Kurtosis6.9063833
Mean147.43832
Median Absolute Deviation (MAD)27
Skewness1.9374713
Sum56174
Variance2517.6837
MonotonicityNot monotonic
2024-02-17T15:26:55.687395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 10
 
1.3%
116 8
 
1.1%
108 8
 
1.1%
132 7
 
0.9%
149 7
 
0.9%
130 7
 
0.9%
140 6
 
0.8%
162 6
 
0.8%
101 6
 
0.8%
150 6
 
0.8%
Other values (138) 310
41.7%
(Missing) 363
48.8%
ValueCountFrequency (%)
62 1
0.1%
71 1
0.1%
73 1
0.1%
74 1
0.1%
75 1
0.1%
76 1
0.1%
78 2
0.3%
80 2
0.3%
82 1
0.1%
83 1
0.1%
ValueCountFrequency (%)
444 2
0.3%
329 1
0.1%
327 2
0.3%
321 1
0.1%
289 1
0.1%
284 1
0.1%
277 1
0.1%
274 1
0.1%
270 1
0.1%
264 2
0.3%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct149
Distinct (%)71.0%
Missing534
Missing (%)71.8%
Infinite0
Infinite (%)0.0%
Mean596.79048
Minimum346
Maximum1494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:55.825791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum346
5-th percentile481.9
Q1534.25
median582
Q3637.75
95-th percentile762.75
Maximum1494
Range1148
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation104.9489
Coefficient of variation (CV)0.17585552
Kurtosis24.940154
Mean596.79048
Median Absolute Deviation (MAD)54
Skewness3.3406747
Sum125326
Variance11014.272
MonotonicityNot monotonic
2024-02-17T15:26:55.968991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515 4
 
0.5%
561 4
 
0.5%
650 4
 
0.5%
548 4
 
0.5%
525 4
 
0.5%
592 4
 
0.5%
637 3
 
0.4%
572 3
 
0.4%
542 3
 
0.4%
693 3
 
0.4%
Other values (139) 174
 
23.4%
(Missing) 534
71.8%
ValueCountFrequency (%)
346 1
0.1%
395 1
0.1%
452 1
0.1%
454 1
0.1%
468 1
0.1%
471 1
0.1%
472 1
0.1%
476 1
0.1%
480 1
0.1%
481 2
0.3%
ValueCountFrequency (%)
1494 1
0.1%
856 1
0.1%
850 1
0.1%
837 1
0.1%
833 1
0.1%
824 2
0.3%
817 1
0.1%
792 1
0.1%
775 1
0.1%
765 1
0.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct175
Distinct (%)75.1%
Missing511
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean1479.6738
Minimum1050
Maximum2445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:56.094316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1050
5-th percentile1190.4
Q11315
median1445
Q31597
95-th percentile1909
Maximum2445
Range1395
Interquartile range (IQR)282

Descriptive statistics

Standard deviation223.52699
Coefficient of variation (CV)0.15106504
Kurtosis1.4777579
Mean1479.6738
Median Absolute Deviation (MAD)139
Skewness0.91851463
Sum344764
Variance49964.316
MonotonicityNot monotonic
2024-02-17T15:26:56.226429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1680 8
 
1.1%
1260 7
 
0.9%
1200 5
 
0.7%
1440 5
 
0.7%
1560 5
 
0.7%
1500 4
 
0.5%
1380 4
 
0.5%
1565 3
 
0.4%
1410 3
 
0.4%
1420 3
 
0.4%
Other values (165) 186
 
25.0%
(Missing) 511
68.7%
ValueCountFrequency (%)
1050 1
0.1%
1058 1
0.1%
1076 1
0.1%
1080 1
0.1%
1113 1
0.1%
1127 1
0.1%
1141 1
0.1%
1163 1
0.1%
1165 1
0.1%
1170 1
0.1%
ValueCountFrequency (%)
2445 1
0.1%
2220 1
0.1%
2100 2
0.3%
2050 1
0.1%
2000 1
0.1%
1980 1
0.1%
1979 1
0.1%
1966 1
0.1%
1940 1
0.1%
1926 1
0.1%

Sprint 400m (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)35.3%
Missing605
Missing (%)81.3%
Infinite0
Infinite (%)0.0%
Mean78.417266
Minimum54
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-02-17T15:26:56.361849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile58.9
Q169
median77
Q387
95-th percentile100.3
Maximum120
Range66
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.117584
Coefficient of variation (CV)0.16727928
Kurtosis-0.039258191
Mean78.417266
Median Absolute Deviation (MAD)9
Skewness0.47602035
Sum10900
Variance172.071
MonotonicityNot monotonic
2024-02-17T15:26:56.546611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
76 10
 
1.3%
75 9
 
1.2%
90 6
 
0.8%
62 5
 
0.7%
82 5
 
0.7%
96 5
 
0.7%
58 5
 
0.7%
86 5
 
0.7%
78 5
 
0.7%
64 5
 
0.7%
Other values (39) 79
 
10.6%
(Missing) 605
81.3%
ValueCountFrequency (%)
54 1
 
0.1%
57 1
 
0.1%
58 5
0.7%
59 3
0.4%
60 1
 
0.1%
61 1
 
0.1%
62 5
0.7%
63 3
0.4%
64 5
0.7%
65 2
 
0.3%
ValueCountFrequency (%)
120 1
0.1%
110 1
0.1%
109 1
0.1%
108 1
0.1%
105 1
0.1%
104 1
0.1%
103 1
0.1%
100 2
0.3%
99 1
0.1%
98 1
0.1%

Interactions

2024-02-17T15:26:47.070852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:28.719397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.005437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.241427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.590694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.407400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.770534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.467270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.673256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.895564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.118097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.395601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.674956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.795761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.164971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:28.830569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.100137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.341200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.761562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.515489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.441144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.556156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.758227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.996982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.209472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.486981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.760117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.925044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.266458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:28.925670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.188026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.429203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.886768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.631288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.521811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.637430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.866368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.078629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.294975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.571316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.837005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.008366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.370917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.015366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.273202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.510720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.026459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.742307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.605055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.727851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.947686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.175660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.396877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.664751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.916965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.105638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.470467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.113845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.364560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.597260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.176481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.858898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.678579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.810551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.035428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.272195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.488920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.749191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.993238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.195709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.580298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.200844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.462224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.693611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.301187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.954717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.763325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.898354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.121278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.363816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.609495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.841912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.072316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.291253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.690317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.282848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.541758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.775360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.443464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.051091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.839441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.975410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.203985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.440096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.695606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.919923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.151359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.369414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.799454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.374136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.628375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.866977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.573516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.148426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:37.920025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.062325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.291753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.518826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.787120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.004223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.227458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.449705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.904618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.462871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.722453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.954162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.688467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.237998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.007492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.150368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.385612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.603109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.879244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.092426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.314038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.540328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:47.994048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.552648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.803819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.039092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.792491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.321931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.083093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.224483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.468555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.684004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.964784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.183057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.396181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.616379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:48.094360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.647312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.892196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.132665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:33.902421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.416186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.165705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.313911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.556524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.770140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.057426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.315242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.480900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.700216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:48.194313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.744177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:30.982891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.228710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.013255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.517506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.242658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.390087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.636123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.870377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.150212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.417226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.555229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.792248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:48.277638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.828272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.059439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.327060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.174334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.599520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.315002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.484933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.722174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:41.959358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.232124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.496610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.631668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.885873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:48.354684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:29.917568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:31.143489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:32.423834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:34.305245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:35.688534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:38.386053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:39.583227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:40.800630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:42.036305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:43.309277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:44.588050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:45.712638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:26:46.981447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T15:26:56.654767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Chad1000x (s)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Games_LevelGrace (s)Helen (s)Max Pull-upsRankRegionRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.0170.8090.7120.271-0.174-0.4870.074-0.491-0.1990.279-0.3170.074-0.1350.717-0.290
Chad1000x (s)0.0171.000-0.444-0.1500.5000.600-0.5710.0000.0000.100-0.5000.1830.0000.800-0.3431.000
Clean and Jerk (lbs)0.809-0.4441.0000.6550.355-0.182-0.4980.078-0.537-0.1860.346-0.3830.078-0.1110.861-0.329
Deadlift (lbs)0.712-0.1500.6551.0000.261-0.175-0.3760.071-0.491-0.2430.264-0.2950.071-0.2470.572-0.357
Fight Gone Bad0.2710.5000.3550.2611.000-0.311-0.3030.000-0.506-0.4100.145-0.3510.000-0.3600.300-0.193
Filthy 50 (s)-0.1740.600-0.182-0.175-0.3111.0000.4130.0000.4290.325-0.3810.2660.0000.073-0.1740.248
Fran (s)-0.487-0.571-0.498-0.376-0.3030.4131.0000.0000.4210.510-0.5220.4030.0000.443-0.4980.482
Games_Level0.0740.0000.0780.0710.0000.0000.0001.000-0.101-0.1620.006-0.2161.0000.0840.0530.002
Grace (s)-0.4910.000-0.537-0.491-0.5060.4290.421-0.1011.0000.358-0.2430.3790.0490.272-0.5620.270
Helen (s)-0.1990.100-0.186-0.243-0.4100.3250.510-0.1620.3581.000-0.4410.3290.2060.493-0.2130.527
Max Pull-ups0.279-0.5000.3460.2640.145-0.381-0.5220.006-0.243-0.4411.000-0.2440.086-0.2510.378-0.261
Rank-0.3170.183-0.383-0.295-0.3510.2660.403-0.2160.3790.329-0.2441.0000.2530.152-0.3870.228
Region0.0740.0000.0780.0710.0000.0000.0001.0000.0490.2060.0860.2531.0000.0840.0530.002
Run 5k (s)-0.1350.800-0.111-0.247-0.3600.0730.4430.0840.2720.493-0.2510.1520.0841.000-0.0560.676
Snatch (lbs)0.717-0.3430.8610.5720.300-0.174-0.4980.053-0.562-0.2130.378-0.3870.053-0.0561.000-0.251
Sprint 400m (s)-0.2901.000-0.329-0.357-0.1930.2480.4820.0020.2700.527-0.2610.2280.0020.676-0.2511.000

Missing values

2024-02-17T15:26:48.501961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T15:26:48.774525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-17T15:26:48.999797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
737Michelle MerandCrossFit FBDVNaNAfricaWomen6.0Africaquarterfinals308.64680231.48510348.32996178.57422NaN47.0NaNNaNNaN151.0140.0NaN1380.062.0
738Angelique ConnowayCrossFit FBDVNaNAfricaWomen9.0Africaquarterfinals308.64680231.48510385.80850182.98346NaNNaNNaNNaNNaNNaNNaNNaN1440.0NaN
739Lani VenterCrossFit 10 StarNaNAfricaWomen10.0Africaquarterfinals297.62370220.46200374.78540176.36960317.047.0NaNNaNNaN171.0135.0622.0NaNNaN
740Dina Swift6th Element CrossFitNaNAfricaWomen11.0Africaquarterfinals319.66990231.48510363.76230198.41580426.035.0NaNNaNNaN170.085.0472.01320.0NaN
741Leilani TisonCrossFit U1NaNAfricaWomen26.0Africaquarterfinals324.07914220.46200374.78540149.91416382.0NaNNaNNaNNaNNaN107.0NaN1482.0NaN
742Simone Van wykCrossFit UnboxedNaNAfricaWomen35.0Africaquarterfinals246.91744187.39270286.60060132.27720NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
743Marilyn MangotoNaNNaNAfricaWomen40.0Africaquarterfinals330.69300242.50820396.83160176.36960NaN50.0NaNNaNNaNNaNNaNNaN1434.0NaN
744Ocean OosthuizenRTF CrossFit KrugersdorpNaNAfricaWomen53.0Africaquarterfinals260.14516194.00656308.64680154.32340NaNNaNNaNNaNNaNNaN109.0NaNNaNNaN
745Marike HartmanCrossFit 4ENaNAfricaWomen133.0Africaquarterfinals253.53130191.80194324.07914143.30030NaNNaNNaNNaNNaN362.0149.0NaN1800.0NaN
746Elzé OlivierCrossFit IraNaNAfricaWomen135.0Africaquarterfinals253.53130187.39270308.64680136.68644NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
44068Gisely PradoPunk CrossFitNaNSouth AmericaWomen128.0South Americaquarterfinals321.87452238.09896396.83160160.93726NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
44069Luana LealCrossFit ThriboNaNSouth AmericaWomen143.0South Americaquarterfinals264.55440222.66662315.26066143.30030NaNNaNNaNNaNNaN225.0169.0NaNNaN72.0
44070Tassia DaddaCrossFit High PulseNaNSouth AmericaWomen156.0South Americaquarterfinals275.57750213.84814313.05604187.39270NaNNaNNaNNaNNaN118.0NaNNaNNaNNaN
44071Mayara RamaldesCrossFit TyphoonNaNSouth AmericaWomen160.0South Americaquarterfinals274.00000185.00000315.00000145.00000NaNNaNNaNNaNNaN271.0NaNNaNNaNNaN
44072Sandra D'AmelioAB CrossFitNaNSouth AmericaWomen217.0South AmericaquarterfinalsNaN175.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
44073Thais AbreuFlamma CrossFitNaNSouth AmericaWomen222.0South Americaquarterfinals268.96364198.41580346.12534125.66334NaN28.0NaNNaNNaN187.0140.0671.0NaNNaN
44074Nicole BonavitaCrossFit CFP9 CasaShoppingNaNSouth AmericaWomen226.0South AmericaquarterfinalsNaNNaNNaNNaNNaN30.0NaNNaNNaN200.0NaNNaNNaN90.0
44075Leisy ChaconCrossFit Yellow FalconNaNSouth AmericaWomen237.0South Americaquarterfinals210.00000165.00000225.00000115.00000NaN16.0NaNNaN1680.0328.0284.0748.01811.0NaN
44076Gabriela DantasCrossFit Ribeirao PretoNaNSouth AmericaWomen246.0South Americaquarterfinals309.00000166.00000342.00000133.00000NaN20.0NaNNaNNaNNaNNaNNaNNaNNaN
44077Alexandra LombardoSpeck CrossFit CCSNaNSouth AmericaWomen247.0South AmericaquarterfinalsNaNNaN285.00000NaNNaNNaNNaNNaNNaNNaN233.0NaNNaNNaN

Duplicate rows

Most frequently occurring

AthleteAffiliateRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)# duplicates
0Jailynn HarkeCrossFit OBANorth America EastWomen796.0North America Eastquarterfinals210.0155.0235.0125.0NaNNaNNaNNaN2327.0345.0224.0NaNNaNNaN2
1Jamie BrunoCrossFit MinneapolisNorth America WestWomen807.0North America Westquarterfinals223.0163.0238.0123.0NaNNaNNaNNaN1916.0359.0444.0NaN1477.078.02
2Maggie BouckaertCrossFit 401North America WestWomen839.0North America Westquarterfinals245.0180.0300.0145.0NaN24.0NaNNaNNaN220.0NaNNaN1597.096.02
3Samantha ReynoldsCrossFit BowmanvilleNorth America EastWomen1436.0North America Eastquarterfinals215.0155.0290.0120.0293.0NaNNaNNaN1454.0283.0177.0693.0NaN96.02
4Sarah BuckleyCrossFit FreeNorth America EastWomen913.0North America Eastquarterfinals230.0180.0300.0145.0271.030.0NaNNaN1272.0846.0125.0650.01680.090.02